Machine and Deep Learning–driven Angular Momentum Inference from BHEX Observations of the n = 1 Photon Ring
The n = 1 photon ring is an important probe of black hole (BH) properties and will be resolved by the Black Hole Explorer (BHEX) for the first time. However, extraction of black hole parameters from observations of the n = 1 subring is not trivial. Developing this capability can be achieved by build...
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IOP Publishing
2025-01-01
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| Series: | The Astrophysical Journal |
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| Online Access: | https://doi.org/10.3847/1538-4357/adbbe3 |
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| author | Joseph Farah Jordy Davelaar Daniel Palumbo Michael Johnson Jonathan Delgado |
| author_facet | Joseph Farah Jordy Davelaar Daniel Palumbo Michael Johnson Jonathan Delgado |
| author_sort | Joseph Farah |
| collection | DOAJ |
| description | The n = 1 photon ring is an important probe of black hole (BH) properties and will be resolved by the Black Hole Explorer (BHEX) for the first time. However, extraction of black hole parameters from observations of the n = 1 subring is not trivial. Developing this capability can be achieved by building a sample of n = 1 subring simulations, as well as by performing feature extraction on this high-volume sample to track changes in the geometry, which presents significant computational challenges. Here, we present a framework for the study of n = 1 photon-ring behavior and BH property measurement from BHEX images. We use KerrBAM to generate a grid of ≳10 ^6 images of n = 1 photon rings spanning the entire space of Kerr BH spins and inclinations. Intensity profiles are extracted from images using a novel feature-extraction method developed specifically for BHEX. This novel method is highly optimized and outperforms existing Event Horizon Telescope methods by a factor of ∼3000. Additionally, we propose a novel, minimal set of geometric measurables for characterizing the behavior of the n = 1 subring geometry. We apply these measurables to our simulation grid and test spin recovery on simulated images using (i) gradient boosting, a machine learning algorithm, and (ii) an extension of Deep Horizon, a deep learning framework. We find ≳90% correct recovery of BH properties using the machine/deep learning approaches, and characterize the space of resolution-dependent geometric degeneracies. Finally, we test both approaches on general relativistic magnetohydrodynamic simulations of black hole accretion flows, and report accurate recovery of spin at the expected inclination of M87*. |
| format | Article |
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| institution | OA Journals |
| issn | 1538-4357 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IOP Publishing |
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| series | The Astrophysical Journal |
| spelling | doaj-art-5b2b859bdc194f7389ee5593ba52bc5c2025-08-20T02:26:55ZengIOP PublishingThe Astrophysical Journal1538-43572025-01-01983218510.3847/1538-4357/adbbe3Machine and Deep Learning–driven Angular Momentum Inference from BHEX Observations of the n = 1 Photon RingJoseph Farah0https://orcid.org/0000-0003-4914-5625Jordy Davelaar1https://orcid.org/0000-0002-2685-2434Daniel Palumbo2https://orcid.org/0000-0002-7179-3816Michael Johnson3https://orcid.org/0000-0002-4120-3029Jonathan Delgado4https://orcid.org/0009-0005-8120-8499Las Cumbres Observatory , 6740 Cortona Drive, Suite 102, Goleta, CA 93117-5575, USA ; josephfarah@ucsb.edu; Department of Physics, University of California , Santa Barbara, CA 93106-9530, USADepartment of Astrophysical Sciences, Peyton Hall, Princeton University , Princeton, NJ 08544, USA; Center for Computational Astrophysics , Flatiron Institute, 162 Fifth Avenue, New York, NY 10010, USA; Department of Astronomy and Columbia Astrophysics Laboratory, Columbia University , 550 W 120th Street, New York, NY 10027, USACenter for Astrophysics ∣ Harvard Smithsonian , 60 Garden Street, Cambridge, MA 02138, USA; Black Hole Initiative at Harvard University , 20 Garden Street, Cambridge, MA 02138, USACenter for Astrophysics ∣ Harvard Smithsonian , 60 Garden Street, Cambridge, MA 02138, USA; Black Hole Initiative at Harvard University , 20 Garden Street, Cambridge, MA 02138, USADepartment of Mathematics, University of California , Irvine, CA 92697, USAThe n = 1 photon ring is an important probe of black hole (BH) properties and will be resolved by the Black Hole Explorer (BHEX) for the first time. However, extraction of black hole parameters from observations of the n = 1 subring is not trivial. Developing this capability can be achieved by building a sample of n = 1 subring simulations, as well as by performing feature extraction on this high-volume sample to track changes in the geometry, which presents significant computational challenges. Here, we present a framework for the study of n = 1 photon-ring behavior and BH property measurement from BHEX images. We use KerrBAM to generate a grid of ≳10 ^6 images of n = 1 photon rings spanning the entire space of Kerr BH spins and inclinations. Intensity profiles are extracted from images using a novel feature-extraction method developed specifically for BHEX. This novel method is highly optimized and outperforms existing Event Horizon Telescope methods by a factor of ∼3000. Additionally, we propose a novel, minimal set of geometric measurables for characterizing the behavior of the n = 1 subring geometry. We apply these measurables to our simulation grid and test spin recovery on simulated images using (i) gradient boosting, a machine learning algorithm, and (ii) an extension of Deep Horizon, a deep learning framework. We find ≳90% correct recovery of BH properties using the machine/deep learning approaches, and characterize the space of resolution-dependent geometric degeneracies. Finally, we test both approaches on general relativistic magnetohydrodynamic simulations of black hole accretion flows, and report accurate recovery of spin at the expected inclination of M87*.https://doi.org/10.3847/1538-4357/adbbe3Black hole physicsHigh energy astrophysicsConvolutional neural networksNeural networks |
| spellingShingle | Joseph Farah Jordy Davelaar Daniel Palumbo Michael Johnson Jonathan Delgado Machine and Deep Learning–driven Angular Momentum Inference from BHEX Observations of the n = 1 Photon Ring The Astrophysical Journal Black hole physics High energy astrophysics Convolutional neural networks Neural networks |
| title | Machine and Deep Learning–driven Angular Momentum Inference from BHEX Observations of the n = 1 Photon Ring |
| title_full | Machine and Deep Learning–driven Angular Momentum Inference from BHEX Observations of the n = 1 Photon Ring |
| title_fullStr | Machine and Deep Learning–driven Angular Momentum Inference from BHEX Observations of the n = 1 Photon Ring |
| title_full_unstemmed | Machine and Deep Learning–driven Angular Momentum Inference from BHEX Observations of the n = 1 Photon Ring |
| title_short | Machine and Deep Learning–driven Angular Momentum Inference from BHEX Observations of the n = 1 Photon Ring |
| title_sort | machine and deep learning driven angular momentum inference from bhex observations of the n 1 photon ring |
| topic | Black hole physics High energy astrophysics Convolutional neural networks Neural networks |
| url | https://doi.org/10.3847/1538-4357/adbbe3 |
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